A Novel Approach of Congestion Management in Deregulated Power System Using an Advanced and Intelligently Trained Twin Extremity Chaotic Map Adaptive Particle Swarm Optimization Algorithm

摘要

This paper addresses the implementation of an advanced twin extremity chaotic map adaptive particle swarm optimization (TECM-PSO) algorithm to the nonlinear congestion management cost problem in deregulated power system. The goal of proposed approach is twofold: firstly, to identify accurate number of participating generators for rescheduling process using a robust upstream real capacity tracing method requiring less information of generator units, and secondly, to achieve minimum possible rescheduled generation cost function using TECM-PSO algorithm while alleviating all the line overloads. Further to preserve the diversity of the algorithm and to increase its near-global searching capability, the incursion of dynamic constraint handling has also been done in the algorithm to retrieve the feasible solutions in the search space. The objective function is solved for near-global optima by step-by-step execution of the proposed algorithm. Twin extremity chaotic maps have been generated by updating the equations governing the PSO algorithm in order to prevent the particle swarm optimization plugging into local minima with less convergence rate at later stages of iterations. The feasibility of the proposed algorithm is validated on various line outage cases of both the small and large test systems, namely modified IEEE 30-, IEEE 57-and IEEE 118-bus systems. Simulation results show a considerable reduction in net rescheduled generation cost, power losses and rescheduled generation amount, ensuring more secure and reliable operation of power system.